Overview

Dataset statistics

Number of variables20
Number of observations295700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 8 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 7 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 10 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with R1 (MOhm) and 8 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 10 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -101.4667999)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32193 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:35:12.471267
Analysis finished2022-12-20 08:37:13.912969
Duration2 minutes and 1.44 second
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45455.048
Minimum0
Maximum90909.553
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:14.041579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4539.0945
Q122731.503
median45462.693
Q368176.642
95-th percentile86366.981
Maximum90909.553
Range90909.553
Interquartile range (IQR)45445.14

Descriptive statistics

Standard deviation26243.232
Coefficient of variation (CV)0.57734473
Kurtosis-1.1996775
Mean45455.048
Median Absolute Deviation (MAD)22722.643
Skewness-0.00024555779
Sum1.3441058 × 1010
Variance6.8870723 × 108
MonotonicityStrictly increasing
2022-12-20T14:07:14.239251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60635.184 1
 
< 0.1%
60606.754 1
 
< 0.1%
60606.449 1
 
< 0.1%
60606.143 1
 
< 0.1%
60605.838 1
 
< 0.1%
60605.533 1
 
< 0.1%
60605.229 1
 
< 0.1%
60604.924 1
 
< 0.1%
60604.62 1
 
< 0.1%
Other values (295690) 295690
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.309 1
< 0.1%
0.618 1
< 0.1%
0.929 1
< 0.1%
1.238 1
< 0.1%
1.544 1
< 0.1%
1.855 1
< 0.1%
2.162 1
< 0.1%
2.471 1
< 0.1%
2.78 1
< 0.1%
ValueCountFrequency (%)
90909.553 1
< 0.1%
90909.248 1
< 0.1%
90908.943 1
< 0.1%
90908.638 1
< 0.1%
90908.334 1
< 0.1%
90908.029 1
< 0.1%
90907.724 1
< 0.1%
90907.419 1
< 0.1%
90907.115 1
< 0.1%
90906.81 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct304
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8987927
Minimum0
Maximum20
Zeros32193
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:14.460749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4278612
Coefficient of variation (CV)0.6493581
Kurtosis-1.2332645
Mean9.8987927
Median Absolute Deviation (MAD)6.67
Skewness0.0095939827
Sum2927073
Variance41.3174
MonotonicityNot monotonic
2022-12-20T14:07:14.616409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32193
10.9%
17.78 29272
9.9%
6.67 29268
9.9%
8.89 29262
9.9%
4.44 29253
9.9%
11.11 29249
9.9%
20 29247
9.9%
2.22 29244
9.9%
13.33 29231
9.9%
15.56 29187
9.9%
Other values (294) 294
 
0.1%
ValueCountFrequency (%)
0 32193
10.9%
0.0533 1
 
< 0.1%
0.2134 1
 
< 0.1%
0.32 1
 
< 0.1%
0.3555 1
 
< 0.1%
0.4174 1
 
< 0.1%
0.4356 1
 
< 0.1%
0.5528 1
 
< 0.1%
0.8125 1
 
< 0.1%
0.9502 1
 
< 0.1%
ValueCountFrequency (%)
20 29247
9.9%
19.9023 1
 
< 0.1%
19.4666 1
 
< 0.1%
19.4184 1
 
< 0.1%
18.82 1
 
< 0.1%
18.7701 1
 
< 0.1%
18.739 1
 
< 0.1%
18.5481 1
 
< 0.1%
18.4937 1
 
< 0.1%
18.0575 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct17886
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.375461
Minimum16.38
Maximum72.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:14.779712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.38
5-th percentile23.07
Q136.128975
median46.61
Q355.27
95-th percentile64.69
Maximum72.88
Range56.5
Interquartile range (IQR)19.141025

Descriptive statistics

Standard deviation12.435753
Coefficient of variation (CV)0.27406339
Kurtosis-0.73687925
Mean45.375461
Median Absolute Deviation (MAD)9.42
Skewness-0.15840268
Sum13417524
Variance154.64795
MonotonicityNot monotonic
2022-12-20T14:07:14.930263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.98 5653
 
1.9%
37.2 2988
 
1.0%
30.76 2936
 
1.0%
36.13 2824
 
1.0%
32.92 2143
 
0.7%
50.74 2136
 
0.7%
48.72 2015
 
0.7%
28.58 2014
 
0.7%
38.26 1991
 
0.7%
31.84 1991
 
0.7%
Other values (17876) 269009
91.0%
ValueCountFrequency (%)
16.38 449
0.2%
16.3801 1
 
< 0.1%
16.3803 1
 
< 0.1%
16.3805 1
 
< 0.1%
16.4102 1
 
< 0.1%
16.4265 1
 
< 0.1%
16.4656 1
 
< 0.1%
16.4998 1
 
< 0.1%
16.581 1
 
< 0.1%
16.5973 1
 
< 0.1%
ValueCountFrequency (%)
72.88 14
 
< 0.1%
72.8774 1
 
< 0.1%
72.8769 1
 
< 0.1%
72.8743 1
 
< 0.1%
72.8739 1
 
< 0.1%
72.8713 1
 
< 0.1%
72.8708 1
 
< 0.1%
72.87 146
< 0.1%
72.8699 1
 
< 0.1%
72.8697 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct4676
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.302234
Minimum25.98
Maximum26.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:15.084846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.98
5-th percentile26.06
Q126.14
median26.34
Q326.42
95-th percentile26.54
Maximum26.62
Range0.64
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.15777888
Coefficient of variation (CV)0.0059986875
Kurtosis-1.2213779
Mean26.302234
Median Absolute Deviation (MAD)0.12
Skewness-0.16978225
Sum7777570.5
Variance0.024894175
MonotonicityNot monotonic
2022-12-20T14:07:15.233998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.38 34322
11.6%
26.42 32032
10.8%
26.46 28495
9.6%
26.06 27287
9.2%
26.14 26592
9.0%
26.5 21082
 
7.1%
26.22 20694
 
7.0%
26.26 17091
 
5.8%
26.18 15061
 
5.1%
26.3 13176
 
4.5%
Other values (4666) 59868
20.2%
ValueCountFrequency (%)
25.98 120
< 0.1%
25.981 1
 
< 0.1%
25.9819 1
 
< 0.1%
25.9828 2
 
< 0.1%
25.9838 1
 
< 0.1%
25.9844 1
 
< 0.1%
25.9848 1
 
< 0.1%
25.9853 1
 
< 0.1%
25.9865 1
 
< 0.1%
25.9876 1
 
< 0.1%
ValueCountFrequency (%)
26.62 944
0.3%
26.6193 1
 
< 0.1%
26.6188 2
 
< 0.1%
26.6185 1
 
< 0.1%
26.6184 1
 
< 0.1%
26.618 1
 
< 0.1%
26.6177 1
 
< 0.1%
26.6174 2
 
< 0.1%
26.6168 1
 
< 0.1%
26.6162 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11494
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94349
Minimum0
Maximum275.6979
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:15.391874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7509
Q1239.8949
median239.972
Q3240.0452
95-th percentile240.1841
Maximum275.6979
Range275.6979
Interquartile range (IQR)0.1503

Descriptive statistics

Standard deviation1.8911779
Coefficient of variation (CV)0.0078817639
Kurtosis11906.441
Mean239.94349
Median Absolute Deviation (MAD)0.075
Skewness-101.4668
Sum70951289
Variance3.576554
MonotonicityNot monotonic
2022-12-20T14:07:15.531060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9686 152
 
0.1%
239.9683 143
 
< 0.1%
239.9583 137
 
< 0.1%
239.9826 135
 
< 0.1%
239.9625 135
 
< 0.1%
239.9821 135
 
< 0.1%
239.988 134
 
< 0.1%
239.968 134
 
< 0.1%
239.9543 131
 
< 0.1%
239.9714 131
 
< 0.1%
Other values (11484) 294333
99.5%
ValueCountFrequency (%)
0 8
< 0.1%
0.0547 1
 
< 0.1%
0.2009 1
 
< 0.1%
0.3482 1
 
< 0.1%
7.659 1
 
< 0.1%
25.7557 1
 
< 0.1%
52.0372 1
 
< 0.1%
80.6487 1
 
< 0.1%
98.3478 1
 
< 0.1%
117.6403 1
 
< 0.1%
ValueCountFrequency (%)
275.6979 1
< 0.1%
264.9723 1
< 0.1%
264.4917 1
< 0.1%
262.3809 1
< 0.1%
259.7318 1
< 0.1%
257.6768 1
< 0.1%
257.4872 1
< 0.1%
257.2554 1
< 0.1%
255.8208 1
< 0.1%
255.5321 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1691
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35486081
Minimum0.198
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:15.694439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.198
5-th percentile0.1991
Q10.2
median0.2
Q30.2069
95-th percentile0.898
Maximum0.9
Range0.702
Interquartile range (IQR)0.0069

Descriptive statistics

Standard deviation0.28836526
Coefficient of variation (CV)0.81261513
Kurtosis-0.20622885
Mean0.35486081
Median Absolute Deviation (MAD)0.0003
Skewness1.3370146
Sum104932.34
Variance0.083154525
MonotonicityNot monotonic
2022-12-20T14:07:15.845241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 129116
43.7%
0.898 34041
 
11.5%
0.199 12519
 
4.2%
0.1996 6231
 
2.1%
0.1999 6205
 
2.1%
0.1994 6173
 
2.1%
0.1992 6168
 
2.1%
0.1997 6164
 
2.1%
0.1998 6116
 
2.1%
0.1993 6094
 
2.1%
Other values (1681) 76873
26.0%
ValueCountFrequency (%)
0.198 2
 
< 0.1%
0.1981 5
< 0.1%
0.1982 6
< 0.1%
0.1983 4
< 0.1%
0.1984 5
< 0.1%
0.1985 6
< 0.1%
0.1986 4
< 0.1%
0.1987 3
< 0.1%
0.1988 2
 
< 0.1%
0.1989 2
 
< 0.1%
ValueCountFrequency (%)
0.9 1
 
< 0.1%
0.8998 1
 
< 0.1%
0.8995 1
 
< 0.1%
0.8992 2
 
< 0.1%
0.899 1608
0.5%
0.8989 1036
0.4%
0.8988 1018
0.3%
0.8987 1033
0.3%
0.8986 987
0.3%
0.8985 1023
0.3%

R1 (MOhm)
Real number (ℝ)

Distinct8490
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.961474
Minimum0.0327
Maximum114.818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:16.008005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.0786
Q10.4175
median1.88
Q327.9071
95-th percentile68.5571
Maximum114.818
Range114.7853
Interquartile range (IQR)27.4896

Descriptive statistics

Standard deviation23.365373
Coefficient of variation (CV)1.4638606
Kurtosis0.89269387
Mean15.961474
Median Absolute Deviation (MAD)1.7981
Skewness1.4323171
Sum4719807.9
Variance545.94068
MonotonicityNot monotonic
2022-12-20T14:07:16.270529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.5638 738
 
0.2%
70.7619 725
 
0.2%
68.5571 712
 
0.2%
71.9176 711
 
0.2%
69.6423 704
 
0.2%
73.1111 703
 
0.2%
71.3877 687
 
0.2%
69.1448 683
 
0.2%
68.0747 663
 
0.2%
67.5047 662
 
0.2%
Other values (8480) 288712
97.6%
ValueCountFrequency (%)
0.0327 1
 
< 0.1%
0.0331 1
 
< 0.1%
0.0332 1
 
< 0.1%
0.0333 2
< 0.1%
0.0335 3
< 0.1%
0.0336 1
 
< 0.1%
0.0339 2
< 0.1%
0.034 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0342 1
 
< 0.1%
ValueCountFrequency (%)
114.818 4
 
< 0.1%
113.4868 4
 
< 0.1%
111.9292 4
 
< 0.1%
110.6632 7
 
< 0.1%
109.181 13
 
< 0.1%
107.9756 12
 
< 0.1%
106.5634 21
< 0.1%
105.4143 41
< 0.1%
104.0673 40
< 0.1%
102.9706 47
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8241
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.973945
Minimum0.0557
Maximum183.4484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:16.429716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0557
5-th percentile0.14
Q10.4901
median1.4441
Q331.0916
95-th percentile77.677
Maximum183.4484
Range183.3927
Interquartile range (IQR)30.6015

Descriptive statistics

Standard deviation27.113505
Coefficient of variation (CV)1.5084894
Kurtosis0.3970459
Mean17.973945
Median Absolute Deviation (MAD)1.3022
Skewness1.3552247
Sum5314895.5
Variance735.14214
MonotonicityNot monotonic
2022-12-20T14:07:16.579018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.5097 1111
 
0.4%
76.9383 1085
 
0.4%
79.7171 1072
 
0.4%
77.677 1042
 
0.4%
79.0683 1027
 
0.3%
78.3034 1023
 
0.3%
81.1822 1014
 
0.3%
82.004 999
 
0.3%
75.6194 958
 
0.3%
75.0345 955
 
0.3%
Other values (8231) 285414
96.5%
ValueCountFrequency (%)
0.0557 1
< 0.1%
0.0569 1
< 0.1%
0.057 1
< 0.1%
0.0577 2
< 0.1%
0.0582 1
< 0.1%
0.0592 1
< 0.1%
0.0594 1
< 0.1%
0.0597 2
< 0.1%
0.0598 1
< 0.1%
0.0601 1
< 0.1%
ValueCountFrequency (%)
183.4484 1
 
< 0.1%
135.8172 1
 
< 0.1%
131.804 1
 
< 0.1%
122.8846 1
 
< 0.1%
121.0628 1
 
< 0.1%
117.8584 2
 
< 0.1%
116.4568 1
 
< 0.1%
114.818 3
< 0.1%
113.4868 7
< 0.1%
111.9292 5
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8232
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.518973
Minimum0.0539
Maximum159.2042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:16.741894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0539
5-th percentile0.1119
Q10.5949
median4.3326
Q345.8407
95-th percentile81.51
Maximum159.2042
Range159.1503
Interquartile range (IQR)45.2458

Descriptive statistics

Standard deviation28.782574
Coefficient of variation (CV)1.2781477
Kurtosis-0.37543089
Mean22.518973
Median Absolute Deviation (MAD)4.2188
Skewness1.0261989
Sum6658860.2
Variance828.43654
MonotonicityNot monotonic
2022-12-20T14:07:16.894061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.0507 1139
 
0.4%
80.6932 1137
 
0.4%
84.6501 1137
 
0.4%
83.7703 1122
 
0.4%
82.2033 1119
 
0.4%
81.51 1111
 
0.4%
85.3974 1063
 
0.4%
80.0247 1049
 
0.4%
79.2369 1015
 
0.3%
78.592 993
 
0.3%
Other values (8222) 284815
96.3%
ValueCountFrequency (%)
0.0539 1
 
< 0.1%
0.0542 1
 
< 0.1%
0.0546 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0566 1
 
< 0.1%
0.0567 1
 
< 0.1%
0.0568 2
< 0.1%
0.057 4
< 0.1%
0.0573 1
 
< 0.1%
0.0574 3
< 0.1%
ValueCountFrequency (%)
159.2042 1
 
< 0.1%
153.6975 1
 
< 0.1%
131.01 1
 
< 0.1%
123.6951 1
 
< 0.1%
118.8647 1
 
< 0.1%
114.1263 2
 
< 0.1%
112.8031 5
 
< 0.1%
111.2549 8
 
< 0.1%
109.9965 18
< 0.1%
108.5233 36
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7612
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.196563
Minimum0.0402
Maximum101.6954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:17.062615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0402
5-th percentile0.1018
Q12.0355
median21.5249
Q334.1241
95-th percentile51.1162
Maximum101.6954
Range101.6552
Interquartile range (IQR)32.0886

Descriptive statistics

Standard deviation17.560505
Coefficient of variation (CV)0.82846002
Kurtosis-0.81367532
Mean21.196563
Median Absolute Deviation (MAD)15.5082
Skewness0.3632294
Sum6267823.7
Variance308.37134
MonotonicityNot monotonic
2022-12-20T14:07:17.216873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.898 970
 
0.3%
32.447 969
 
0.3%
33.8093 966
 
0.3%
34.4447 963
 
0.3%
36.1399 959
 
0.3%
32.6048 959
 
0.3%
35.7879 952
 
0.3%
33.033 950
 
0.3%
34.9216 950
 
0.3%
33.3338 949
 
0.3%
Other values (7602) 286113
96.8%
ValueCountFrequency (%)
0.0402 1
< 0.1%
0.0404 2
< 0.1%
0.0411 2
< 0.1%
0.0413 1
< 0.1%
0.0415 2
< 0.1%
0.042 2
< 0.1%
0.0422 1
< 0.1%
0.0423 1
< 0.1%
0.0424 1
< 0.1%
0.0425 1
< 0.1%
ValueCountFrequency (%)
101.6954 1
 
< 0.1%
94.1082 1
 
< 0.1%
83.6839 1
 
< 0.1%
82.6836 3
 
< 0.1%
81.8678 2
 
< 0.1%
80.9098 2
 
< 0.1%
80.1281 5
 
< 0.1%
79.2097 11
< 0.1%
78.4601 13
< 0.1%
77.5789 24
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7891
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0338
Minimum0.049
Maximum169.2115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:17.381641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.049
5-th percentile0.115
Q11.8241
median33.3292
Q352.3895
95-th percentile79.5571
Maximum169.2115
Range169.1625
Interquartile range (IQR)50.5654

Descriptive statistics

Standard deviation27.46547
Coefficient of variation (CV)0.85739031
Kurtosis-0.98609427
Mean32.0338
Median Absolute Deviation (MAD)25.4339
Skewness0.34436088
Sum9472394.7
Variance754.35204
MonotonicityNot monotonic
2022-12-20T14:07:17.532973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0682 1342
 
0.5%
48.3116 1328
 
0.4%
48.8555 1310
 
0.4%
49.9804 1308
 
0.4%
49.7203 1296
 
0.4%
52.1043 1287
 
0.4%
46.5203 1286
 
0.4%
47.0253 1272
 
0.4%
48.6068 1268
 
0.4%
49.4117 1267
 
0.4%
Other values (7881) 282736
95.6%
ValueCountFrequency (%)
0.049 1
 
< 0.1%
0.0494 1
 
< 0.1%
0.0496 1
 
< 0.1%
0.05 1
 
< 0.1%
0.0501 1
 
< 0.1%
0.0502 1
 
< 0.1%
0.0505 2
< 0.1%
0.0507 3
< 0.1%
0.051 1
 
< 0.1%
0.0511 1
 
< 0.1%
ValueCountFrequency (%)
169.2115 1
 
< 0.1%
129.7955 1
 
< 0.1%
127.7625 1
 
< 0.1%
124.1949 4
 
< 0.1%
122.6378 4
 
< 0.1%
120.8197 5
 
< 0.1%
119.345 7
 
< 0.1%
117.6218 13
< 0.1%
116.223 16
< 0.1%
114.5874 27
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7893
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.92007
Minimum0.048
Maximum191.4518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:17.701412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.048
5-th percentile0.124
Q11.5669
median23.2627
Q349.8802
95-th percentile78.4368
Maximum191.4518
Range191.4038
Interquartile range (IQR)48.3133

Descriptive statistics

Standard deviation27.309899
Coefficient of variation (CV)0.94432342
Kurtosis-0.8828047
Mean28.92007
Median Absolute Deviation (MAD)23.12
Skewness0.54720401
Sum8551664.6
Variance745.83059
MonotonicityNot monotonic
2022-12-20T14:07:17.854125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.1206 1169
 
0.4%
79.1164 1154
 
0.4%
80.6531 1127
 
0.4%
48.6935 1127
 
0.4%
77.6364 1126
 
0.4%
49.2798 1121
 
0.4%
50.2138 1121
 
0.4%
49.0116 1118
 
0.4%
46.4788 1118
 
0.4%
79.9474 1111
 
0.4%
Other values (7883) 284408
96.2%
ValueCountFrequency (%)
0.048 1
 
< 0.1%
0.0483 1
 
< 0.1%
0.0484 1
 
< 0.1%
0.0486 1
 
< 0.1%
0.0487 1
 
< 0.1%
0.0491 1
 
< 0.1%
0.0492 1
 
< 0.1%
0.0504 1
 
< 0.1%
0.0509 3
< 0.1%
0.0511 1
 
< 0.1%
ValueCountFrequency (%)
191.4518 1
 
< 0.1%
168.4491 1
 
< 0.1%
129.6516 1
 
< 0.1%
123.7317 1
 
< 0.1%
120.179 1
 
< 0.1%
118.6302 1
 
< 0.1%
116.8232 3
 
< 0.1%
115.3585 4
 
< 0.1%
113.6483 4
 
< 0.1%
112.2611 16
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7777
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.181926
Minimum0.0529
Maximum138.9553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:18.022436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0529
5-th percentile0.1222
Q11.9287
median32.3678
Q353.0601
95-th percentile80.5302
Maximum138.9553
Range138.9024
Interquartile range (IQR)51.1314

Descriptive statistics

Standard deviation27.803348
Coefficient of variation (CV)0.86394295
Kurtosis-1.0446345
Mean32.181926
Median Absolute Deviation (MAD)26.0192
Skewness0.34968294
Sum9516195.5
Variance773.02616
MonotonicityNot monotonic
2022-12-20T14:07:18.270925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4536 1321
 
0.4%
50.8483 1302
 
0.4%
48.8606 1299
 
0.4%
49.6787 1299
 
0.4%
49.1134 1296
 
0.4%
52.4174 1294
 
0.4%
49.4202 1294
 
0.4%
49.9924 1286
 
0.4%
51.7898 1285
 
0.4%
50.2568 1282
 
0.4%
Other values (7767) 282742
95.6%
ValueCountFrequency (%)
0.0529 1
 
< 0.1%
0.0533 2
< 0.1%
0.0534 1
 
< 0.1%
0.0539 1
 
< 0.1%
0.0543 1
 
< 0.1%
0.0545 4
< 0.1%
0.0547 1
 
< 0.1%
0.0551 1
 
< 0.1%
0.0552 3
< 0.1%
0.0553 1
 
< 0.1%
ValueCountFrequency (%)
138.9553 1
 
< 0.1%
132.8873 1
 
< 0.1%
116.9118 2
 
< 0.1%
115.5214 4
 
< 0.1%
113.8957 3
 
< 0.1%
112.5752 15
 
< 0.1%
111.0301 23
 
< 0.1%
109.7743 34
< 0.1%
108.304 63
< 0.1%
107.1083 72
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6234
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.786405
Minimum0.0326
Maximum96.8414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:18.445227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0326
5-th percentile0.0996
Q112.1976
median27.8405
Q341.9221
95-th percentile62.5385
Maximum96.8414
Range96.8088
Interquartile range (IQR)29.7245

Descriptive statistics

Standard deviation20.250031
Coefficient of variation (CV)0.72877478
Kurtosis-0.80269392
Mean27.786405
Median Absolute Deviation (MAD)14.2831
Skewness0.15846042
Sum8216440
Variance410.06377
MonotonicityNot monotonic
2022-12-20T14:07:18.598667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1002 1044
 
0.4%
47.6519 1021
 
0.3%
54.8097 1005
 
0.3%
49.7495 1005
 
0.3%
46.2986 1003
 
0.3%
53.3555 1001
 
0.3%
0.1005 997
 
0.3%
48.5445 996
 
0.3%
39.4289 995
 
0.3%
51.0146 992
 
0.3%
Other values (6224) 285641
96.6%
ValueCountFrequency (%)
0.0326 1
< 0.1%
0.0333 1
< 0.1%
0.0334 1
< 0.1%
0.0335 2
< 0.1%
0.0337 1
< 0.1%
0.0338 2
< 0.1%
0.0339 2
< 0.1%
0.0342 1
< 0.1%
0.0343 1
< 0.1%
0.0344 1
< 0.1%
ValueCountFrequency (%)
96.8414 1
 
< 0.1%
95.604 1
 
< 0.1%
91.3229 1
 
< 0.1%
90.4024 1
 
< 0.1%
89.3217 5
 
< 0.1%
88.4405 8
 
< 0.1%
87.4055 12
< 0.1%
86.5611 14
< 0.1%
85.5689 18
< 0.1%
84.7592 25
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6164
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.798559
Minimum0.0289
Maximum78.6328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:18.827518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0289
5-th percentile0.0967
Q18.4052
median22.2411
Q336.6026
95-th percentile58.3888
Maximum78.6328
Range78.6039
Interquartile range (IQR)28.1974

Descriptive statistics

Standard deviation18.664601
Coefficient of variation (CV)0.78427443
Kurtosis-0.69687447
Mean23.798559
Median Absolute Deviation (MAD)14.3442
Skewness0.39605016
Sum7037233.9
Variance348.36734
MonotonicityNot monotonic
2022-12-20T14:07:18.990321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0972 3163
 
1.1%
0.0973 3101
 
1.0%
0.0971 3066
 
1.0%
0.0975 2953
 
1.0%
0.097 2914
 
1.0%
0.0968 2771
 
0.9%
0.0976 2623
 
0.9%
0.0967 2443
 
0.8%
0.0977 2344
 
0.8%
0.0966 2285
 
0.8%
Other values (6154) 268037
90.6%
ValueCountFrequency (%)
0.0289 1
 
< 0.1%
0.0291 1
 
< 0.1%
0.0294 1
 
< 0.1%
0.0297 3
< 0.1%
0.0298 1
 
< 0.1%
0.0299 5
< 0.1%
0.03 3
< 0.1%
0.0301 2
 
< 0.1%
0.0302 3
< 0.1%
0.0303 5
< 0.1%
ValueCountFrequency (%)
78.6328 7
 
< 0.1%
77.9697 6
 
< 0.1%
77.1883 3
 
< 0.1%
76.5489 10
 
< 0.1%
75.7953 9
 
< 0.1%
75.1784 18
 
< 0.1%
74.4511 23
 
< 0.1%
73.8555 26
 
< 0.1%
73.1532 51
< 0.1%
72.5778 71
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6449
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.057525
Minimum0.0369
Maximum103.4404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:19.334229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0369
5-th percentile0.118
Q17.785
median24.0606
Q340.4326
95-th percentile63.7159
Maximum103.4404
Range103.4035
Interquartile range (IQR)32.6476

Descriptive statistics

Standard deviation20.754063
Coefficient of variation (CV)0.79647098
Kurtosis-0.74889733
Mean26.057525
Median Absolute Deviation (MAD)16.372
Skewness0.40517955
Sum7705210.3
Variance430.73113
MonotonicityNot monotonic
2022-12-20T14:07:19.482723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1199 2011
 
0.7%
0.1198 1975
 
0.7%
0.1197 1910
 
0.6%
0.1201 1852
 
0.6%
0.1195 1834
 
0.6%
0.1194 1769
 
0.6%
0.1202 1739
 
0.6%
0.1204 1724
 
0.6%
0.1193 1644
 
0.6%
0.1191 1479
 
0.5%
Other values (6439) 277763
93.9%
ValueCountFrequency (%)
0.0369 1
 
< 0.1%
0.0377 1
 
< 0.1%
0.0378 2
< 0.1%
0.038 1
 
< 0.1%
0.0381 1
 
< 0.1%
0.0383 2
< 0.1%
0.0385 2
< 0.1%
0.0386 1
 
< 0.1%
0.0388 2
< 0.1%
0.039 3
< 0.1%
ValueCountFrequency (%)
103.4404 1
 
< 0.1%
102.3503 1
 
< 0.1%
100.0304 1
 
< 0.1%
93.6563 1
 
< 0.1%
90.6767 2
 
< 0.1%
89.8357 6
 
< 0.1%
88.8467 3
 
< 0.1%
88.0388 8
< 0.1%
87.0883 14
< 0.1%
86.3116 17
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6202
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.985805
Minimum0.0311
Maximum103.7538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:19.639991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0311
5-th percentile0.108
Q110.6647
median27.7396
Q342.7901
95-th percentile63.909
Maximum103.7538
Range103.7227
Interquartile range (IQR)32.1254

Descriptive statistics

Standard deviation20.773679
Coefficient of variation (CV)0.74229341
Kurtosis-0.84000706
Mean27.985805
Median Absolute Deviation (MAD)15.2444
Skewness0.19922163
Sum8275402.5
Variance431.54573
MonotonicityNot monotonic
2022-12-20T14:07:19.793353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1085 2980
 
1.0%
0.1086 2884
 
1.0%
0.1084 2862
 
1.0%
0.1088 2862
 
1.0%
0.1082 2564
 
0.9%
0.1089 2421
 
0.8%
0.1081 2307
 
0.8%
0.108 2038
 
0.7%
0.109 1910
 
0.6%
0.1078 1751
 
0.6%
Other values (6192) 271121
91.7%
ValueCountFrequency (%)
0.0311 1
< 0.1%
0.0312 1
< 0.1%
0.0316 2
< 0.1%
0.0317 1
< 0.1%
0.0319 1
< 0.1%
0.032 1
< 0.1%
0.0321 1
< 0.1%
0.0323 1
< 0.1%
0.0324 1
< 0.1%
0.0325 2
< 0.1%
ValueCountFrequency (%)
103.7538 1
 
< 0.1%
98.1088 1
 
< 0.1%
90.1079 1
 
< 0.1%
88.3056 2
 
< 0.1%
87.3522 15
 
< 0.1%
86.5731 18
< 0.1%
85.6562 17
 
< 0.1%
84.9067 23
< 0.1%
84.0241 32
< 0.1%
83.3024 44
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6270
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.020332
Minimum0.033
Maximum109.0714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:19.951754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.1075
Q19.8698
median26.4004
Q339.8842
95-th percentile56.5195
Maximum109.0714
Range109.0384
Interquartile range (IQR)30.0144

Descriptive statistics

Standard deviation18.984548
Coefficient of variation (CV)0.72960437
Kurtosis-0.82243287
Mean26.020332
Median Absolute Deviation (MAD)14.2364
Skewness0.13501527
Sum7694212.1
Variance360.41305
MonotonicityNot monotonic
2022-12-20T14:07:20.105461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1088 1321
 
0.4%
0.1086 1284
 
0.4%
0.1087 1276
 
0.4%
0.109 1263
 
0.4%
0.1091 1228
 
0.4%
0.1093 1166
 
0.4%
0.1094 1158
 
0.4%
0.1084 1155
 
0.4%
0.1082 1089
 
0.4%
0.1096 1087
 
0.4%
Other values (6260) 283673
95.9%
ValueCountFrequency (%)
0.033 1
 
< 0.1%
0.0335 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.034 1
 
< 0.1%
0.0341 3
< 0.1%
0.0342 1
 
< 0.1%
0.0343 2
< 0.1%
0.0344 2
< 0.1%
ValueCountFrequency (%)
109.0714 1
 
< 0.1%
86.7475 3
 
< 0.1%
85.8287 4
 
< 0.1%
85.0777 8
 
< 0.1%
84.1934 14
 
< 0.1%
83.4702 30
< 0.1%
82.6184 30
< 0.1%
81.9217 37
< 0.1%
81.1007 46
< 0.1%
80.4289 49
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6350
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.849304
Minimum0.0337
Maximum84.1371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:20.268170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0337
5-th percentile0.1011
Q17.9033
median21.9959
Q335.06
95-th percentile53.501
Maximum84.1371
Range84.1034
Interquartile range (IQR)27.1567

Descriptive statistics

Standard deviation17.485115
Coefficient of variation (CV)0.76523621
Kurtosis-0.71170166
Mean22.849304
Median Absolute Deviation (MAD)13.3569
Skewness0.3173942
Sum6756539.1
Variance305.72923
MonotonicityNot monotonic
2022-12-20T14:07:20.415197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.103 1245
 
0.4%
0.1027 1237
 
0.4%
0.1029 1194
 
0.4%
0.1026 1169
 
0.4%
0.1032 1156
 
0.4%
0.1031 1131
 
0.4%
0.1034 1121
 
0.4%
0.1025 1114
 
0.4%
0.1023 1082
 
0.4%
0.1039 1075
 
0.4%
Other values (6340) 284176
96.1%
ValueCountFrequency (%)
0.0337 1
< 0.1%
0.0342 1
< 0.1%
0.0343 1
< 0.1%
0.0344 1
< 0.1%
0.0345 1
< 0.1%
0.0346 1
< 0.1%
0.0348 2
< 0.1%
0.0349 2
< 0.1%
0.035 1
< 0.1%
0.0351 1
< 0.1%
ValueCountFrequency (%)
84.1371 1
 
< 0.1%
76.0113 3
 
< 0.1%
75.4135 4
 
< 0.1%
74.7083 14
 
< 0.1%
74.1305 2
 
< 0.1%
73.4487 16
 
< 0.1%
72.8899 12
 
< 0.1%
72.2303 28
< 0.1%
71.6896 35
< 0.1%
71.0511 45
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6197
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.909811
Minimum0.032
Maximum99.8467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:07:20.672788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.032
5-th percentile0.107
Q19.7787
median27.3206
Q345.0239
95-th percentile68.3837
Maximum99.8467
Range99.8147
Interquartile range (IQR)35.2452

Descriptive statistics

Standard deviation22.307585
Coefficient of variation (CV)0.77162682
Kurtosis-0.90850806
Mean28.909811
Median Absolute Deviation (MAD)17.5796
Skewness0.29125727
Sum8548631.1
Variance497.62837
MonotonicityNot monotonic
2022-12-20T14:07:20.823899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1077 2665
 
0.9%
0.1075 2660
 
0.9%
0.1078 2631
 
0.9%
0.1079 2551
 
0.9%
0.1074 2502
 
0.8%
0.108 2493
 
0.8%
0.1082 2471
 
0.8%
0.1072 2395
 
0.8%
0.1071 2047
 
0.7%
0.1083 1991
 
0.7%
Other values (6187) 271294
91.7%
ValueCountFrequency (%)
0.032 1
 
< 0.1%
0.0321 1
 
< 0.1%
0.0322 1
 
< 0.1%
0.0326 3
< 0.1%
0.0327 2
 
< 0.1%
0.0328 1
 
< 0.1%
0.0329 4
< 0.1%
0.0332 3
< 0.1%
0.0333 5
< 0.1%
0.0334 3
< 0.1%
ValueCountFrequency (%)
99.8467 1
 
< 0.1%
91.4606 1
 
< 0.1%
89.5776 1
 
< 0.1%
86.9716 2
 
< 0.1%
86.0327 5
 
< 0.1%
85.2654 2
 
< 0.1%
84.3623 9
 
< 0.1%
83.6241 10
 
< 0.1%
82.7549 29
< 0.1%
82.0441 59
< 0.1%

Interactions

2022-12-20T14:07:05.345095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:56.705735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.038291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:03.690117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.117589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:10.589985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.110064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:17.734690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:21.235935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:25.132382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:28.776973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.360726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.068367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:39.779007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:43.496105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:47.150712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:50.916406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:54.558953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:58.234865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:01.752841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:05.511508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:56.882829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.218518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:03.853396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.280342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:10.757108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.265169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:17.904295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:21.383824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:25.305646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:28.945416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.530821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.236751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:39.947569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:43.661236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:47.324143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:51.083886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:54.730208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:58.386150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:01.924362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:05.698892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.043859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.399540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.009154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.452288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:10.937231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.455293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:18.081483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:21.645118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:25.499689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:29.125425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.728024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.429661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:40.139254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:43.852343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:47.517808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:51.274821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:54.921821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:58.569200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.107544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:05.869382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.200323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.567634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.179167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.621903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:11.110409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.633896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:18.358666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:21.868871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:25.675127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:29.298777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.903385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.610966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:40.313532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:44.013905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:47.696234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:51.440909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:55.110172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:58.735500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.277498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:06.055247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.349118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.733624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.347665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.819503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:11.279185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.820573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:18.522623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:22.053135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:25.862647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:29.480591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:33.091213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.797051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:40.603211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:44.197196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:47.889574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:51.628434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:55.284334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:58.919990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.457042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:06.211667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.516026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:00.908415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.510262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:07.990141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:11.447658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:14.988978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:18.692935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:22.226903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:26.031935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:29.741567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:33.262090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:36.981131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:40.764621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:44.370689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:48.072399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:51.797148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:55.454913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:59.098051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.625845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:06.445289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.684465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:01.087378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.675034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:08.167527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:11.624022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:15.177009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:18.874943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:22.459352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:26.229343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:29.924792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:33.455241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:37.172669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:40.958326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:44.558750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:48.262495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:52.096489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:55.641445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:59.281991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.814073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:06.624410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.856086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:01.255384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:04.851658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:08.347004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:11.788401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:15.354199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:19.043835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:22.637725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:26.404642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:30.095711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:33.643316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:37.354664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:41.137232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:44.728730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:48.473613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:52.259880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:55.814510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:59.458258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:02.986020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:06.836137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:57.999483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:01.661911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:05.022286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:08.519327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:06:10.261287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:13.778506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:17.379834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:20.900595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:24.792043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:28.431968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.025017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:35.724462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:39.437674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:43.152035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:46.814393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:50.561902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:54.220135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:57.789825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:01.395064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:05.013845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:08.878078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:05:59.877612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:03.529781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:06.957107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:10.417661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:13.938418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:17.546711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:21.036519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:24.959364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:28.597187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:32.193487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:35.892498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:39.602058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:43.320801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:46.970288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:50.745669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:54.379159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:06:57.955227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:01.565341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:07:05.175853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:07:20.983077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:07:21.257396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:07:21.522000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:07:21.787597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:07:22.054788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:07:09.172801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:07:10.326120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.049.7526.62243.42810.19960.47260.54640.74335.12376.22563.37535.164741.922137.611646.063250.245440.463335.352851.2656
10.3090.049.7526.62242.10580.20000.46520.54080.72914.82465.76613.19744.837544.247936.277843.524446.701541.237636.092151.5462
20.6180.049.7526.62241.76490.19920.45860.53560.71644.56935.39833.03494.502743.287936.132145.358351.105631.995735.192522.4640
30.9290.049.7526.62241.42190.20000.07390.16990.12820.16760.16500.16730.13810.56080.24800.33530.18480.16940.14340.1404
41.2380.049.7526.62241.09200.87120.06270.15070.10600.10650.11050.13570.11450.12350.10810.13370.12060.12230.10960.1139
51.5440.049.7526.62241.12690.88930.06990.14470.10520.10160.10740.12900.11590.10980.10130.12280.11430.11660.10660.1110
61.8550.049.7526.62241.16230.89450.07550.14100.10720.10030.10890.12600.11790.10780.10010.12120.11280.11530.10640.1103
72.1620.049.7526.62241.19730.89700.07930.13960.11000.10060.11120.12490.12010.10760.09980.12100.11230.11490.10670.1100
82.4710.049.7526.62241.24000.89720.08200.13990.11300.10140.11350.12490.12230.10750.09960.12130.11170.11490.10680.1100
92.7800.049.7526.62241.28480.89800.08430.14070.11600.10250.11550.12550.12450.10760.09940.12130.11160.11480.10720.1099
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29569090906.8100.062.7826.50.20090.21400.77171.98023.29572.25653.25643.69005.06157.39117.11678.798413.348417.799918.363224.7048
29569190907.1150.062.7826.50.05470.20905.311011.802819.172911.140017.550819.095023.948941.682831.640744.174247.210952.442240.016057.4449
29569290907.4190.062.7826.50.00000.205019.710935.621650.358522.905143.262547.864655.404761.076944.801863.715960.760767.436948.117868.3837
29569390907.7240.062.7826.50.00000.203041.279060.561575.163829.797065.897067.015673.747263.521649.053067.664667.869768.488249.477569.4831
29569490908.0290.062.7826.50.00000.201458.502270.248677.831730.947370.619878.436880.530260.659750.242271.484464.855869.075450.373070.6179
29569590908.3340.062.7826.50.00000.201067.036875.034580.024731.481270.107679.947479.863162.538550.242270.335663.403968.006450.373069.4831
29569690908.6380.062.7826.50.00000.200667.504774.344477.209131.067167.369174.225876.320363.978649.912366.083263.909066.416949.477570.0976
29569790908.9430.062.7826.50.00000.200062.686971.387769.222830.178663.973367.513071.340061.585051.781365.631863.909064.037749.477569.4831
29569890909.2480.062.7826.50.00000.200257.317867.036866.633028.976960.439961.985663.084658.423554.890371.484464.335960.883148.914668.9791
29569990909.5530.062.7826.50.00000.200054.460662.686962.798727.962556.132457.649058.818359.761453.478372.126762.989059.601848.117867.8952